Open AccessOncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses
Author(s) -
Joshua Broyde,
David Simpson,
Diana Murray,
Evan Paull,
Brennan Chu,
Somnath Tagore,
Sunny J. Jones,
Aaron T. Griffin,
Federico M. Giorgi,
Alexander Lachmann,
Peter K. Jackson,
E. Alejandro Sweet-Cordero,
Barry Honig,
Andrea Califano
Publication year - 2020
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/s41587-020-0652-7
Subject(s) - kras , biology , computational biology , effector , crosstalk , context (archaeology) , gene , cancer research , genetics , microbiology and biotechnology , mutation , paleontology , physics , optics
Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.